Typically, neural networks are trained using labeled input data that has been selected based on a particular field of interest. For example, a neural network trained to recognize human faces within images may be trained using a corpus of data including images of human faces. Additional examples include using brain scan images (e.g., Magnetic Resonance Imaging (“MM”) images) to train a neural network to detect strokes, images of particular animals to train a neural network to identify those animals within images, and using street images to train a neural network for autonomous driving.
However, the data used to train neural networks, such as those mentions above, are biased in that they only include inputs that relate to the specific desired outputs of the neural network. For example, when training a neural network to identify dogs or cats within an image, a training set of images of dogs or cats may be used. Here, the neural network is inherently biased due to the input images including the objects to be identified by the neural network. As another example, when training a neural network model to reconstruct brain scans, the neural network may be trained using a collection of selected brain scans.
Additionally, obtaining training data for a neural network can be an expensive and laborious process. For instance, labeling of data for use to train a neural network can take a long period of time and can also be inherently biased. In the case of training a neural network to reconstruct brain scans, the training data must be analyzed carefully by a skilled individual in order to properly label each brain scan. These and other drawbacks exist.